Potential of vermicomposting with mixtures of animal manure and vegetable leaves in the development of Eisenia foetida, microbial biomass, and enzymatic activity under semi-arid conditions.

Vermicomposting is the bio-oxidation and stabilization of organic matter involving relationships between the action of earthworms and microorganisms and the activation and dynamics of several enzyme activities. Semi-arid farmers to make (extra) money and organic production, produce their vermicompost using plant residues and animal manure, but there is no information about the final product generated. Thus, this study aimed to analyze the potential of vermicomposting with mixtures of animal manure and vegetable leaves in the development of Eisenia foetida, microbial biomass, and enzymatic activity in the semi-arid region, Brazil. The experimental design applied was randomized block in a 6 × 4 factorial scheme with four replicates, with six treatments (mixtures of cattle manure, goat manure, cashew leaves, and catanduva leaves) and evaluated at four-time intervals (30, 60, 90, and 120 days of vermicomposting). The treatments were placed in polyethylene pots in the same site, environmental conditions, and residues proportions as used by farmers. The characteristics analyzed were the number of earthworms (NE), total earthworm biomass (TEB) and earthworm multiplication index (MI), microbial biomass carbon (MBC), and activities of enzymes ß-glucosidase, dehydrogenase, alkaline and acid phosphatases. The cattle manure vermicomposted shows the highest average values observed for NE, MI, TEB, MBC, and enzymatic activity, regardless of the plant leaves mix. In general, the enzymes activities were found in the descending order of ß-glucosidase > alkaline phosphatase > dehydrogenase > acid phosphatase. The maturation dynamics of vermicompost were characterized by a decline in the microbial population and number and biomass of earthworms in the substrate and consequently a decrease in new enzyme synthesis and degradation of the remaining enzyme pool. Microbial biomass and enzymatic activity were indicators for changes in the quality of vermicompost.

Urothelial Carcinoma on a Ureterocele: Case Report and Review of the Literature.

Urothelial carcinoma on a ureterocele is extremely rare in the literature, and few case reports have been reported. There are no guidelines for diagnosis and management, and current practice is extrapolated from bladder and upper urothelial tract carcinoma. We present a case from a 61-year-old man with urothelial carcinoma on a ureterocele treated with ureterocele resection, distal urethrectomy, and reimplantation on the bladder. We also review the literature concerning diagnostic approaches and management.

Optic Disc Localization in Retinal Images Based on Cumulative Sum Fields.

This paper describes an automatic method for the optic disc localization in retinal images, which is effective and reliable with multiple datasets. Particularly, the described method reveals very effective dealing with retinal images with large pathological signs. The algorithm begins with a new vessel enhancement method based on a modified corner detector. Subsequently, a weighted version of the vessel enhancement is combined with morphological operators, to detect the four main vessels orientations {0(°), 45(°), 90(°), 135(°) }. These four image functions have all the necessary information to determine an initial optic disc localization, resulting in two images that are respectively divided along the vertical or horizontal orientations with different division sizes. Each division is averaged creating a 2-D step function, and a cumulative sum of the different sizes step functions is calculated in the vertical and horizontal orientations, resulting in an initial optic disc position. The final optic disc localization is determined by a vessel convergence algorithm using its two most relevant features; high vasculature convergence and high intensity values. The proposed method was evaluated in eight publicly available datasets, including the STARE and DRIVE datasets. The optic disc was localized correctly in 1752 out of the 1767 retinal images (99.15%) with an average computation time of 18.34 s.

A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis.

Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.

Piecewise approximation of contours through scale-space selection of dominant points.

This paper describes a method of approximating a shape contour with a polygon. The polygon vertices are extracted from the curvature extremes, through a scale-space description of the contour, via linear diffusion. These vertices are located on the contour points where the sharper changes of the contour directions occur. Using a proper strategy, a set of extremes that result in a given approximation level is chosen. By adding new vertices, the approximation level can be improved, and a scalable representation of the contour is identified. This method results in an approximation that discriminates local from global geometric features and provides a good visual representation of the original contour. This polygonal approximation method is used for scalable encoding of the shape contours. In this regard, an encoding technique suitable for scalable polygonal approximation has been developed. We show that encoding the approximated polygons result in a good relation between the distortion and the bitrate. Finally, we show that in addition to coding this method can be efficiently used for shape comparison and shape retrieval.